Abstract: Weld quality is primarily determined from the weld bead geometry. This work concerns the weld bead geometry prediction in pulsed metal inert gas welding (PMIGW) process. A back propagation neural network (BPNN) model, a radial basis function network (RBFN) model and regression model have been developed to predict the weld bead geometry of welded plates. Six process parameters, namely pulse voltage, back-ground voltage, pulse duty factor, pulse frequency, wire feed rate and the welding speed along with root mean square (RMS) values of two sensor signals, namely the welding current and the voltage signals, are used as input variables of the two models. The weld bead width, height and reinforcement of the welded plate are considered as the output variables. Having same process parameters does not always result in the same output quality. This is why, inclusion of sensor signals in the models, as developed in this work, results in better output prediction.
Keywords: PMIGW, weld bead geometry prediction, back propagation neural network, radial basis function network, regression model, response surface methodology
